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Running the experiments

  1. Edit the EXPERIMENT CONFIGURATION section of the analysis.py file to set the appropriate experimental setup.
  2. Run python3 analysis.py configuration_experiments [experiment_name] script (the first argument is the name of the file with the selected configuration, and the second argument, optional, is an additional name for the experiment logs folder).

If you do not want that python uses buffered output, which is useful when you want to see stdout logs as soon as they are produced, especially when the stdout is written to a file (e.g. nohup), where large buffers are used that may retain the output for a while, run python with -u option (unbuffered).

For example, python3 -u analysis.py configuration_experiments KNN

The models developed in this study are:

  • PPIIBM_first_item, Pair Prediction by Item Identification Baseline Model (first item mode)
  • PPIIBM_both_items, Pair Prediction by Item Identification Baseline Model (both items mode)

The classic machine learning models that can be selected are:

  • KNN, k-nearest neighbors
  • LR, logistic regression classifier
  • RF, random forest classifier
  • SVC, Support Vector Classifier

Creating the virtual environment

Python venv

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Running with GPU

It is necessary to have Conda previously installed on your system.

Creating the Conda environment with RAPIDS:

conda create -n rapids-24.02 -c rapidsai -c conda-forge -c nvidia cuml=24.02 python=3.10 cuda-version=11.8

Once the environment is activated, you can run the Python scripts to use RAPIDS and execute them on GPU by changing the use_GPU = True flag in the experiment configuration file.